class_docstr: The heterogeneous H&M dataset from the `Kaggle H&M Personalized Fashion
  Recommendations
  <https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations>`_
  challenge.
  The task is to develop product recommendations based on data from previous
  transactions, as well as from customer and product meta data.
  
  Args:
      root (str): Root directory where the dataset should be saved.
      use_all_tables_as_node_types (bool, optional): If set to :obj:`True`,
          will use the transaction table as a distinct node type.
          (default: :obj:`False`)
      transform (callable, optional): A function/transform that takes in an
          :obj:`torch_geometric.data.HeteroData` object and returns a
          transformed version. The data object will be transformed before
          every access. (default: :obj:`None`)
      pre_transform (callable, optional): A function/transform that takes in
          an :obj:`torch_geometric.data.HeteroData` object and returns a
          transformed version. The data object will be transformed before
          being saved to disk. (default: :obj:`None`)
      force_reload (bool, optional): Whether to re-process the dataset.
          (default: :obj:`False`)
code: def processed_file_names(self) -> str:
      if self.use_all_tables_as_node_types:
          return "data.pt"
      else:
          return "data_merged.pt"
  
file_path: pytorch_geometric/torch_geometric/datasets/hm.py
imports: from typing import Callable, List, Optional
  import torch
  from torch_geometric.data import HeteroData, InMemoryDataset
label_desc: Use image node_id 3 for calling the HM obj's underlying member method code with example usage: obj.processed_file_names() and returns: str, str